{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:57:26Z","timestamp":1761631046938,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["821MS125"],"award-info":[{"award-number":["821MS125"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["ZDYF2021SHFZ239"],"award-info":[{"award-number":["ZDYF2021SHFZ239"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research Project \u201copen competition mechanism\u201d of Hainan Medical College","award":["JBGS202107","JBGS202113"],"award-info":[{"award-number":["JBGS202107","JBGS202113"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDB 38040200"],"award-info":[{"award-number":["XDB 38040200"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81871346","81971602","82022036","91959130","81971776","81771924","62027901","81930053"],"award-info":[{"award-number":["81871346","81971602","82022036","91959130","81971776","81771924","62027901","81930053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"crossref","award":["GJJSTD20170004","QYZDJ-SSW-JSC005"],"award-info":[{"award-number":["GJJSTD20170004","QYZDJ-SSW-JSC005"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2017175"],"award-info":[{"award-number":["2017175"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Comput. Ind. Biomed. Art"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (<jats:italic>v<\/jats:italic><jats:sub><jats:italic>e<\/jats:italic><\/jats:sub>), volume fraction of plasma space (<jats:italic>v<\/jats:italic><jats:sub><jats:italic>p<\/jats:italic><\/jats:sub>), volume transfer constant (<jats:italic>K<\/jats:italic><jats:sup><jats:italic>trans<\/jats:italic><\/jats:sup>), and reverse reflux rate constant (<jats:italic>k<\/jats:italic><jats:sub><jats:italic>ep<\/jats:italic><\/jats:sub>) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model\u2019s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%\u201352.6% and could significantly stratify the risk levels of patients with NPC (<jats:italic>p<\/jats:italic>\u2009=\u20090.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.<\/jats:p>","DOI":"10.1186\/s42492-023-00149-0","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T01:02:49Z","timestamp":1701392569000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma"],"prefix":"10.1186","volume":"6","author":[{"given":"Hailin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Priya S.","family":"Balasubramanian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuebin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0783-3171","authenticated-orcid":false,"given":"Di","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"149_CR1","doi-asserted-by":"publisher","unstructured":"Chen YP, Chan ATC, Le QT, Blanchard P, Sun Y, Ma J (2019) Nasopharyngeal carcinoma. Lancet 394(10192):64\u201380. https:\/\/doi.org\/10.1016\/S0140-6736(19)30956-0","DOI":"10.1016\/S0140-6736(19)30956-0"},{"key":"149_CR2","doi-asserted-by":"publisher","unstructured":"Emanuel O, Liu J, Schartinger VH, Nei WL, Chan YY, Tsang CM et al (2021)\u00a0SSTR2 in nasopharyngeal carcinoma: relationship with latent EBV infection and\u00a0potential as a therapeutic target. Cancers 13(19):4944.\u00a0https:\/\/doi.org\/10.3390\/cancers13194944","DOI":"10.3390\/cancers13194944"},{"key":"149_CR3","doi-asserted-by":"publisher","unstructured":"Lee AWM, Ng WT, Chan JYW, Corry J, M\u00e4kitie A, Mendenhall WM et al (2019) Management of locally recurrent nasopharyngeal carcinoma. Cancer Treat Rev 79:101890.\u00a0https:\/\/doi.org\/10.1016\/j.ctrv.2019.101890","DOI":"10.1016\/j.ctrv.2019.101890"},{"key":"149_CR4","doi-asserted-by":"publisher","unstructured":"Cao XJ, Song J, Xu J, Gong GZ, Yang XH, Su Y et al (2021) Tumor blood flow is a predictor of radiotherapy response in patients with nasopharyngeal carcinoma. Front Oncol 11:567954.\u00a0https:\/\/doi.org\/10.3389\/fonc.2021.567954","DOI":"10.3389\/fonc.2021.567954"},{"key":"149_CR5","doi-asserted-by":"publisher","unstructured":"Wang HY, Sun BY, Zhu ZH, Chang ET, To KF, Hwang JSG et al (2011) Eight-signature classifier for prediction of nasopharyngeal carcinoma survival. J Clin Oncol 29(34):4516\u20134525.\u00a0https:\/\/doi.org\/10.1200\/JCO.2010.33.7741","DOI":"10.1200\/JCO.2010.33.7741"},{"key":"149_CR6","doi-asserted-by":"publisher","unstructured":"Shen LJ, Li W, Wang SY, Xie GF, Zeng Q, Chen C et al (2016) Image-based multilevel subdivision of M1 category in TNM staging system for metastatic nasopharyngeal carcinoma. Radiology 280(3):805\u2013814.\u00a0https:\/\/doi.org\/10.1148\/radiol.2016151344","DOI":"10.1148\/radiol.2016151344"},{"key":"149_CR7","doi-asserted-by":"publisher","unstructured":"Huang WY, Zhang QH, Wu G, Chen PP, Li J, Gillen KM et al (2021) DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1\u03b1, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients. Radiother Oncol 164:146\u2013154.\u00a0https:\/\/doi.org\/10.1016\/j.radonc.2021.09.016","DOI":"10.1016\/j.radonc.2021.09.016"},{"key":"149_CR8","doi-asserted-by":"publisher","unstructured":"Mui AWL, Lee AWM, Lee VHF, Ng WT, Vardhanabhuti V, Man SSY et al (2021) Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Magn Reson Imaging 83:50\u201356.\u00a0https:\/\/doi.org\/10.1016\/j.mri.2021.07.003","DOI":"10.1016\/j.mri.2021.07.003"},{"key":"149_CR9","doi-asserted-by":"publisher","unstructured":"Zheng DC, Chen YB, Liu XY, Chen Y, Xu LY, Ren W et al (2015) Early response to chemoradiotherapy for nasopharyngeal carcinoma treatment: value of dynamic contrast\u2010enhanced 3.0 T MRI. J Magn Reson Imaging 41(6):1528\u20131540.\u00a0https:\/\/doi.org\/10.1002\/jmri.24723","DOI":"10.1002\/jmri.24723"},{"key":"149_CR10","doi-asserted-by":"publisher","unstructured":"Qin YH, Yu XP, Hou J, Hu Y, Li FP, Wen L et al (2019) Prognostic value of the pretreatment primary lesion quantitative dynamic contrast-enhanced magnetic resonance imaging for nasopharyngeal carcinoma. Acad Radiol 26(11):1473\u20131482.\u00a0https:\/\/doi.org\/10.1016\/j.acra.2019.01.021","DOI":"10.1016\/j.acra.2019.01.021"},{"key":"149_CR11","doi-asserted-by":"publisher","unstructured":"You SH, Choi SH, Kim TM, Park CK, Park SH, Won JK et al (2018) Differentiation of high grade from low-grade astrocytoma: improvement in diagnostic accuracy and reliability of pharmacokinetic parameters from DCE MR imaging by using arterial input functions obtained from DSC MR imaging. Radiology 286(3):981\u2013991.\u00a0https:\/\/doi.org\/10.1148\/radiol.2017170764","DOI":"10.1148\/radiol.2017170764"},{"key":"149_CR12","doi-asserted-by":"publisher","unstructured":"Othman AE, Falkner F, Kessler DE, Martirosian P, Weiss J, Kruck S et al (2016) Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: effects on pharmacokinetic parameters and their diagnostic performance. Magn Reson Imaging 34(4):496\u2013501.\u00a0https:\/\/doi.org\/10.1016\/j.mri.2015.12.009","DOI":"10.1016\/j.mri.2015.12.009"},{"key":"149_CR13","doi-asserted-by":"publisher","unstructured":"Azahaf M, Haberley M, Betrouni N, Ernst O, Behal H, Duhamel A et al (2016) Impact of arterial input function selection on the accuracy of dynamic contrast\u2010enhanced MRI quantitative analysis for the diagnosis of clinically significant prostate cancer. J Magn Reson Imaging 43(3):737\u2013749.\u00a0https:\/\/doi.org\/10.1002\/jmri.25034","DOI":"10.1002\/jmri.25034"},{"key":"149_CR14","doi-asserted-by":"publisher","unstructured":"Kumar V, Gu YH, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234\u20131248.\u00a0https:\/\/doi.org\/10.1016\/j.mri.2012.06.010","DOI":"10.1016\/j.mri.2012.06.010"},{"key":"149_CR15","doi-asserted-by":"publisher","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749\u2013762.\u00a0https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"149_CR16","doi-asserted-by":"publisher","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563\u2013577.\u00a0https:\/\/doi.org\/10.1148\/radiol.2015151169","DOI":"10.1148\/radiol.2015151169"},{"key":"149_CR17","doi-asserted-by":"publisher","unstructured":"Cattell R, Ying J, Lei L, Ding J, Chen SL, Sosa MS et al (2022) Preoperative prediction of lymph node metastasis using deep learning-based features. Vis Comput Ind Biomed Art 5(1):8.\u00a0https:\/\/doi.org\/10.1186\/s42492-022-00104-5","DOI":"10.1186\/s42492-022-00104-5"},{"key":"149_CR18","doi-asserted-by":"publisher","unstructured":"Zhang L, Dong D, Li HL, Tian J, Ouyang FS, Mo XK, et al (2019) Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: a retrospective cohort study. eBioMedicine 40:327\u2013335.\u00a0https:\/\/doi.org\/10.1016\/j.ebiom.2019.01.013","DOI":"10.1016\/j.ebiom.2019.01.013"},{"key":"149_CR19","doi-asserted-by":"publisher","unstructured":"Zhong LZ, Fang XL, Dong D, Peng H, Fang MJ, Huang CL et al (2020) A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0. Radiother Oncol 151:1\u20139.\u00a0https:\/\/doi.org\/10.1016\/j.radonc.2020.06.050","DOI":"10.1016\/j.radonc.2020.06.050"},{"key":"149_CR20","doi-asserted-by":"publisher","unstructured":"Zhong LZ, Dong D, Fang XL, Zhang F, Zhang N, Zhang LW et al (2021) A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: a multicentre study. eBioMedicine 70:103522.\u00a0https:\/\/doi.org\/10.1016\/j.ebiom.2021.103522","DOI":"10.1016\/j.ebiom.2021.103522"},{"key":"149_CR21","doi-asserted-by":"publisher","unstructured":"Dong D, Zhang F, Zhong LZ, Fang MJ, Huang CL, Yao JJ et al (2019) Development and validation of a novel MR imaging predictor of response to induction chemotherapy in locoregionally advanced nasopharyngeal cancer: a randomized controlled trial substudy (NCT01245959). BMC Med 17(1):190.\u00a0https:\/\/doi.org\/10.1186\/s12916-019-1422-6","DOI":"10.1186\/s12916-019-1422-6"},{"key":"149_CR22","doi-asserted-by":"publisher","unstructured":"Wang ZP, Fang MJ, Zhang J, Tang LQ, Zhong LZ, Li HL et al (2023) Radiomics and deep learning in nasopharyngeal carcinoma: a review. IEEE Rev Biomed Eng (in press). https:\/\/doi.org\/10.1109\/RBME.2023.3269776","DOI":"10.1109\/RBME.2023.3269776"},{"key":"149_CR23","doi-asserted-by":"publisher","unstructured":"Zhou BL, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, 27-30 June 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"},{"key":"149_CR24","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, IEEE, Venice, 22-29 October 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"149_CR25","doi-asserted-by":"publisher","unstructured":"Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA 112(46):E6265\u2013E6273.\u00a0https:\/\/doi.org\/10.1073\/pnas.1505935112","DOI":"10.1073\/pnas.1505935112"},{"key":"149_CR26","doi-asserted-by":"publisher","unstructured":"Zhao X, Liang YJ, Zhang X, Wen DX, Fan W, Tang LQ et al (2022) Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 49(8):2972\u20132982.\u00a0https:\/\/doi.org\/10.1007\/s00259-022-05793-x","DOI":"10.1007\/s00259-022-05793-x"},{"key":"149_CR27","doi-asserted-by":"publisher","unstructured":"Li HL, Wang SW, Liu B, Fang MJ, Cao RN, He BX et al (2023) A multi-view co-training network for semi-supervised medical image-based prognostic prediction. Neural Networks 164:455\u2013463.\u00a0https:\/\/doi.org\/10.1016\/j.neunet.2023.04.030","DOI":"10.1016\/j.neunet.2023.04.030"},{"key":"149_CR28","doi-asserted-by":"publisher","unstructured":"Yang KX, Tian JF, Zhang B, Li M, Xie WJ, Zou YT et al (2019) A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma. Oral Oncol 98:85\u201391.\u00a0https:\/\/doi.org\/10.1016\/j.oraloncology.2019.09.022","DOI":"10.1016\/j.oraloncology.2019.09.022"},{"key":"149_CR29","doi-asserted-by":"publisher","unstructured":"Zhang B, Tian J, Dong D, Gu DS, Dong YH, Zhang L et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23(15):4259\u20134269.\u00a0https:\/\/doi.org\/10.1158\/1078-0432.CCR-16-2910","DOI":"10.1158\/1078-0432.CCR-16-2910"},{"key":"149_CR30","doi-asserted-by":"publisher","unstructured":"Liu J, Mao Y, Li ZJ, Zhang DK, Zhang ZC, Hao SN et al (2016) Use of texture analysis based on contrast\u2010enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44(2):445\u2013455.\u00a0https:\/\/doi.org\/10.1002\/jmri.25156","DOI":"10.1002\/jmri.25156"},{"key":"149_CR31","doi-asserted-by":"publisher","unstructured":"Wang GY, He L, Yuan C, Huang YQ, Liu ZY, Liang CH (2018) Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol 98:100\u2013106.\u00a0https:\/\/doi.org\/10.1016\/j.ejrad.2017.11.007","DOI":"10.1016\/j.ejrad.2017.11.007"},{"key":"149_CR32","doi-asserted-by":"publisher","unstructured":"Zhao LN, Gong J, Xi YB, Xu M, Li C, Kang XW et al (2020) MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma. Eur Radiol 30(1):537\u2013546.\u00a0https:\/\/doi.org\/10.1007\/s00330-019-06211-x","DOI":"10.1007\/s00330-019-06211-x"},{"issue":"3","key":"149_CR33","first-page":"171","volume":"5","author":"CH Wang","year":"2018","unstructured":"Wang CH, Sun WZ, Kirkpatrick J, Chang Z, Yin FF (2018) Assessment of concurrent stereotactic radiosurgery and bevacizumab treatment of recurrent malignant gliomas using multi-modality MRI imaging and radiomics analysis. J Radiosurg SBRT 5(3):171\u2013181","journal-title":"J Radiosurg SBRT"},{"key":"149_CR34","doi-asserted-by":"publisher","unstructured":"Montemezzi S, Benetti G, Bisighin MV, Camera L, Zerbato C, Caumo F et al (2021) 3T DCE-MRI radiomics improves predictive models of complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol 11:630780.\u00a0https:\/\/doi.org\/10.3389\/fonc.2021.630780","DOI":"10.3389\/fonc.2021.630780"},{"key":"149_CR35","doi-asserted-by":"publisher","unstructured":"Palmisano A, Esposito A, Rancoita PMV, Di Chiara A, Passoni P, Slim N et al (2018) Could perfusion heterogeneity at dynamic contrast-enhanced MRI be used to predict rectal cancer sensitivity to chemoradiotherapy? Clin Radiol 73(10):911.e1\u2013911.e7.\u00a0https:\/\/doi.org\/10.1016\/j.crad.2018.06.007","DOI":"10.1016\/j.crad.2018.06.007"},{"key":"149_CR36","doi-asserted-by":"publisher","unstructured":"Jiang HT, Piao YF, Ye ZM, Jiang CE, Jiang YM, Wang FZ (2022) Development and validation of a pre-treatment magnetic resonance imaging radiomics-based signature to predict progression-free survival in patients with locally advanced nasopharyngeal carcinoma. SSRN 4156709.\u00a0https:\/\/doi.org\/10.2139\/ssrn.4156709","DOI":"10.2139\/ssrn.4156709"},{"key":"149_CR37","doi-asserted-by":"publisher","unstructured":"Shen HS, Wang Y, Liu DH, Lv RF, Huang YY, Peng C et al (2020) Predicting progression-free survival using MRI-based radiomics for patients with nonmetastatic nasopharyngeal carcinoma. Front Oncol 10:618.\u00a0https:\/\/doi.org\/10.3389\/fonc.2020.00618","DOI":"10.3389\/fonc.2020.00618"},{"key":"149_CR38","doi-asserted-by":"publisher","unstructured":"Kim MJ, Choi Y, Sung YE, Lee YS, Kim YS, Ahn KJ et al (2021) Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics. Transl Oncol 14(10):101180.\u00a0https:\/\/doi.org\/10.1016\/j.tranon.2021.101180","DOI":"10.1016\/j.tranon.2021.101180"},{"key":"149_CR39","doi-asserted-by":"publisher","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107.\u00a0https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"149_CR40","doi-asserted-by":"publisher","unstructured":"Meyer HJ, Hamerla G, Leifels L, H\u00f6hn AK, Surov A (2019) Histogram analysis parameters derived from DCE-MRI in head and neck squamous cell cancer\u2013Associations with microvessel density. Eur J Radiol 120:108669.\u00a0https:\/\/doi.org\/10.1016\/j.ejrad.2019.108669","DOI":"10.1016\/j.ejrad.2019.108669"},{"key":"149_CR41","doi-asserted-by":"publisher","unstructured":"Malamas AS, Jin EL, Gujrati M, Lu ZR (2016) Dynamic contrast enhanced MRI assessing the antiangiogenic effect of silencing HIF-1\u03b1 with targeted multifunctional ECO\/siRNA nanoparticles. Mol Pharmaceutics 13(7):2497\u20132506.\u00a0https:\/\/doi.org\/10.1021\/acs.molpharmaceut.6b00227","DOI":"10.1021\/acs.molpharmaceut.6b00227"},{"key":"149_CR42","doi-asserted-by":"publisher","unstructured":"Jansen JFA, Lu YG, Gupta G, Lee NY, Stambuk HE, Mazaheri Y et al (2016) Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer. World J Radiol 8(1):90\u201397.\u00a0https:\/\/doi.org\/10.4329\/wjr.v8.i1.90","DOI":"10.4329\/wjr.v8.i1.90"},{"key":"149_CR43","doi-asserted-by":"publisher","unstructured":"Cui CY, Wang SX, Zhou J, Dong AN, Xie F, Li HJ et al (2020) Machine learning analysis of image data based on detailed MR image reports for nasopharyngeal carcinoma prognosis. Biomed Res Int 2020:8068913.\u00a0https:\/\/doi.org\/10.1155\/2020\/8068913","DOI":"10.1155\/2020\/8068913"},{"key":"149_CR44","doi-asserted-by":"publisher","unstructured":"Gkika E, Benndorf M, Oerther B, Mohammad F, Beitinger S, Adebahr S et al (2020) Immunohistochemistry and radiomic features for survival prediction in small cell lung cancer. Front Oncol 10:1161.\u00a0https:\/\/doi.org\/10.3389\/fonc.2020.01161","DOI":"10.3389\/fonc.2020.01161"},{"key":"149_CR45","doi-asserted-by":"publisher","unstructured":"Dong D, Fang MJ, Tang L, Shan XH, Gao JB, Giganti F, et al (2020) Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol 31(7):912\u2013920.\u00a0https:\/\/doi.org\/10.1016\/j.annonc.2020.04.003","DOI":"10.1016\/j.annonc.2020.04.003"},{"key":"149_CR46","doi-asserted-by":"publisher","unstructured":"Akram F, Koh PE, Wang FQ, Zhou SQ, Tan SH, Paknezhad M et al (2020) Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy. PLoS One 15(10):e0240043.\u00a0https:\/\/doi.org\/10.1371\/journal.pone.0240043","DOI":"10.1371\/journal.pone.0240043"},{"key":"149_CR47","doi-asserted-by":"publisher","unstructured":"Pepe MS, Fan J, Feng ZD, Gerds T, Hilden J (2015) The net reclassification index (NRI): a misleading measure of prediction improvement even with independent test data sets. Stat Biosci 7(2):282\u2013295.\u00a0https:\/\/doi.org\/10.1007\/s12561-014-9118-0","DOI":"10.1007\/s12561-014-9118-0"}],"container-title":["Visual Computing for Industry, Biomedicine, and Art"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-023-00149-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42492-023-00149-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-023-00149-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T01:03:20Z","timestamp":1701392600000},"score":1,"resource":{"primary":{"URL":"https:\/\/vciba.springeropen.com\/articles\/10.1186\/s42492-023-00149-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["149"],"URL":"https:\/\/doi.org\/10.1186\/s42492-023-00149-0","relation":{},"ISSN":["2524-4442"],"issn-type":[{"type":"electronic","value":"2524-4442"}],"subject":[],"published":{"date-parts":[[2023,12,1]]},"assertion":[{"value":"29 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant conflicts of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23"}}